Webinar title: Location-Specific Source Impact Estimation Using Adjoint Sensitivity Analysis

Speaker: Amir Hakami

Webinar time: September 23, 2024 (Monday) 10:00

Venue: Room 208, New Environmental Building

Inviter: Zhen Cheng

Abstract:

Air pollution policies are often developed with various objectives in mind, such as mitigating climate impacts, population health, or nonattainment of standards. These different objectives are often subject to varying forms of parameters and constraints, rendering  coordination of strategies to address multiple endpoints challenging. For example, climate mitigation options are indifferent to the location of the release of pollutants, while emission controls for population health interventions are highly location-dependent. We discuss the use of a full-complexity approach, i.e., adjoint sensitivity analysis or reverse influence modeling, to estimate location-specific source impacts across various endpoints. We examine the principles of adjoint sensitivity analysis and its implementation in the U.S. EPA’s Community Multiscale Air Quality (CMAQ) model, CMAQ-ADJ model, and how the method can lead to location-specific source impact estimates. Our discussion will primarily focus on the use of CMAQ-ADJ in estimating location-specific population health benefits or benefit-per-ton (BPT) of emission reductions, as well as air quality co-benefits of CO₂ mitigation from combustion sources. We show examples of the information contained in adjoint-based co-benefit estimates in Canada and the U.S., and how various assumptions may affect these estimations. We will further show preliminary results of location-specific sectoral co-benefits in China and the Northern Hemisphere, using the hemispheric version of CMAQ-ADJ, and discuss how the adjoint model offers opportunities for devising synergistic climate and population health strategies.

About the speaker:

Amir Hakami is a Professor in the Department of Civil and Environmental Engineering at Carleton University in Ottawa, Canada. His expertise is in air quality modeling, and his research focus is on applications of models to inform air pollution decision-making. While widely recognized as a model developer, much of his work is placed at the interface of various disciplines such as atmospheric modeling, population health, environmental economics, and environmental justice. Amir’s research has been funded by various public and private agencies and institutions such as NSERC, NFRF, Health Canada, Environment and Climate Change Canada, Transport Canada, Health Effects Institute, American Petroleum Institute, World Resources Institute, among others. His postgraduate training includes a Ph.D. in Environmental Engineering from Georgia Institute of Technology, and postdoctoral fellowship at California Institute of Technology. He has held various academic appointments including the Associate Dean of Research at the Faculty of Engineering and Design at Carleton.